Showing papers in "Automatic Control and Computer Sciences in 2018"

Digital Manufacturing Security Indicators

Abstract: This paper describes security indicators specific to digital manufacturing. We divided the set of indicators into three groups: self-similarity-based security indicators, sustainability indicators, and indicators characterizing the homeostatic ability of cyberphysical systems that form the basis of digital production. Indicators can be applied to any type of digital production systems to detect security problems, control the sustainability of their operation, and maintain resilience.

Threat Analysis of Cyber Security in Wireless Adhoc Networks Using Hybrid Neural Network Model

Abstract: The article discusses the problem of analysis of cybersecurity threats in wireless ad hoc networks—VANET, FANET, MARINET, MANET, WSN. The problem of neural network approximation of the function of cyber threat existence in the system is formulated. The parameters of the neural network model were optimized according to the likelihood maximization criterion on the training data set. A hybrid neural network based on recurrent and graph convolutional neural networks is proposed as a solution architecture.

The Problem of Innovative Development of Information Security Systems in the Transport Sector

Abstract: The structure of the process and the model for the formation of strategy and a program of innovative development of the information security system in the transport sector are considered.

Security Analysis Based on Controlling Dependences of Network Traffic Parameters by Wavelet Transformation

Abstract: This article presents mathematical tools of wavelet transformations for use in detecting network traffic attacks. The technique consists in discrete wavelet transformation of parameters of network packets extracted from traffic and tracking the degree of dependence of various network traffic parameters using the multiple correlation coefficient. The efficiency of the proposed technique is shown in the results of experimental detections of SYN flood DoS attacks.

Multifractal Analysis of Internet Backbone Traffic for Detecting Denial of Service Attacks

Abstract: This paper proposes to use multifractal analysis to detect backbone network traffic anomalies that indicate network failures or attacks. Multifractal spectrum characteristics are used as security metrics. The effectiveness of the proposed approach is confirmed by experimental results on detecting Denial of Service attacks.

Detection of Threats in Cyberphysical Systems Based on Deep Learning Methods Using Multidimensional Time Series

Abstract: A method for detecting anomalies in the work of cyberphysical systems by analyzing a multidimensional time series is proposed. The method is based on the use of neural network technologies to predict the values ​​of the time series of the system data and to identify deviations between the predicted value and the current data obtained from the sensors and actuators. The results of experimental studies are presented, which testify to the effectiveness of the proposed solution.

Problems of Security in Digital Production and Its Resistance to Cyber Threats

Abstract: This paper considers digital production as a stage of industrialization. One aspect of this process is the associated digital transformation of control systems. To analyze the problem of ensuring the security of digital production, the concept of cyberphysical systems is used. The perspectives of using homeostatic control of the stability of such systems using self-similarity indicators are considered.

Prevention of Attacks on Dynamic Routing in Self-Organizing Adhoc Networks Using Swarm Intelligence

Abstract: A technology for preventing a full range of attacks on routing in self-organizing adhoc networks (MANET, VANET/FANET/MARINET, IoT, WSN, mesh networks, M2M networks, etc.) is presented. The new technology develops the Watchdog method and the method of estimating the packet transfer coefficient (P-Secure) by implementing an ant swarm algorithm for constructing a secure route in the network, in which all nodes are agents for analyzing the security of neighboring nodes. An example is given of constructing a safe route in the VANET network using the created ant swarm algorithm.

A New Technique for Intelligent Constructing Exact γ-content Tolerance Limits with Expected (1 – α)-confidence on Future Outcomes in the Weibull Case Using Complete or Type II Censored Data

Abstract: The logical purpose for a statistical tolerance limit is to predict future outcomes for some (say, production) process. The coverage value γ is the percentage of the future process outcomes to be captured by the prediction, and the confidence level (1 – α) is the proportion of the time we hope to capture that percentage γ. Tolerance limits of the type mentioned above are considered in this paper, which presents a new technique for constructing exact statistical (lower and upper) tolerance limits on outcomes (for example, on order statistics) in future samples. Attention is restricted to the two-parameter Weibull distribution under parametric uncertainty. The technique used here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. It does not require the construction of any tables and is applicable whether the experimental data are complete or Type II censored. The exact tolerance limits on order statistics associated with sampling from underlying distributions can be found easily and quickly making tables, simulation, Monte Carlo estimated percentiles, special computer programs, and approximation unnecessary. The proposed technique is based on a probability transformation and pivotal quantity averaging. It is conceptually simple and easy to use. The discussion is restricted to one-sided tolerance limits. Finally, we give numerical examples, where the proposed analytical methodology is illustrated in terms of the two-parameter Weibull distribution. Applications to other log-location-scale distributions could follow directly.

Performance Analysis of M2M Traffic in LTE Network Using Queuing Systems with Random Resource Requirements

Abstract: We analyze a multiserver queuing system, in which customers require a server and a certain amount of limited resources for the duration of their service. For the case of discrete resources, we develop a recurrence algorithm to evaluate the model’s stationary probability distribution and its various stationary characteristics, such as the blocking probability and the average amount of occupied resources. The algorithm is applied to analysis of M2M traffic characteristics in a LTE network cell. We derive the cumulative distribution function of radio resource requirements of M2M devices and propose a sampling approach in order to apply the recurrence algorithm to the case of continuous resources.

Enhanced Dynamic Load Balancing Algorithm in Computer Networks with Quality of Services

Abstract: Nowadays there is an increase in transfer volumes of audio, video and other types of multimedia content in modern computer networks (CN). Multimedia streaming and multicasting applications have limited jitter and end-to-end delay requirements and do not allow full providing of all necessary conditions for efficient data transmission on the Internet. To provide quality of service (QoS) of streaming applications Internet Engineering Task Force (IETF) proposed two QoS architectures: IntServ and DiffServ. However, these architectures have the limited ability of dynamical change in the movement of data flows and do not ensure compliance with all QoS requirements. Complexity in service of CN is often combined with incomplete compatibility of network decisions that involves dependence on the hardware manufacturers. The purpose of the work is the development of the enhanced dynamic load balancing algorithm in CN with QoS. The present work is dedicated to the simulation of software of dynamic load balancing processes. The results of the investigations of the offered algorithm on different topology of CN by 5 QoS-metrics are presented too. The assessment and comparison of the efficiency of the offered load-balancing algorithm with known analog such as Yen’s algorithm including traffic engineering (TE) module are executed.

Application Model of Modern Artificial Neural Network Methods for the Analysis of Information Systems Security

Abstract: In this work considered the problem of safety analysis of control mechanisms in modern information systems, including control software systems of cyberphysical and industrial facilities, digital control systems for distributed cyber environments VANET, FANET, MARINET, industrial Internet of things and sensor networks. The representation of security violation as a property of the system described by a complex function is proposed, in which the method of finding violations is described in the form of approximation of that function and the calculation of its values for specific systems. Various approaches to the interpolation of such function are considered in the work, it is shown that the most promising option is the use of deep neural networks.

A Distributed Intrusion Detection System with Protection from an Internal Intruder

Abstract: The protection of modern distributed information networks from external and internal intruders continues to be of great importance due to the development of data transmission and processing technology. The article describes a model of data processing in the distributed intrusion detection system (DIDS) and method of using of hidden agents to protect from an internal intruder. The distribution of the functions on data processing between the DIDS local agent and central data processing node is presented. We describe a method of hiding of presence of the agent from the system user while retaining control of it from the part of the operator.

QoS-Based Concurrent User-Service Grouping for Web Service Recommendation

Abstract: Recently, tremendous growth of web services to share the program, data and resources requires the optimal recommendation strategy. The major issues observed in existing recommendation strategies are scalability, sparsity and the cold start. The employment of matrix factorization (MF) models addressed all the issues effectively. But, they increase the scalability of the system. This paper proposes the new framework that contains web service grouping, distance estimation, service utilization level estimation and the item-to-item comparison (Pearson Correlation Coefficient (PCC)) to improve the recommendation performance. The grouping of users according to the Haversine distance formulation to reduce the complexity in the relevant web service recommendation against the complex queries. The locations and the fields in the services utilization in proposed work provide the effective recommendation performance. The comparative analysis between the proposed novel recommendation framework with the existing techniques assures the effectiveness of proposed approach in web service recommendation.

Using Neural Networks to Detect Internal Intruders in VANETs

Abstract: This article considers ensuring protection of Vehicular Ad-Hoc Networks (VANET) against malicious nodes. Characteristic performance features of VANETs and threats are analyzed, and current attacks identified. The proposed approach to security provision relies on radial basis neural networks and makes it possible to identify malicious nodes by indicators of behavior.

A Blockchain Decentralized Public Key Infrastructure Model

Abstract: This article presents a comparative analysis of available solutions in authenticating access subjects in computer-aided information and telecommunication systems. Centralized and decentralized public key infrastructures (PKIs) are considered, and their weaknesses are brought to light. A blockchain model of decentralized PKI for access subject authentication in computer-aided information and telecommunication systems is proposed. The current embodiments of blockchain PKIs are checked for compliance with the proposed model.

Cyber-sustainability of Software-Defined Networks Based on Situational Management

Abstract: The correctness and efficiency of the homeostatic approach to ensuring cyber-sustainability based on the software-defined networking (SDN) technology is proven. The cyberphysical system Smart Home is simulated using this technology. The authors conducted a series of experiments in which the system was countering various attacks. The software-defined network used three self-adaptation mechanisms to ensure cyber-sustainability in the experiment. The experiments confirmed that the SDN-based Smart Home system retained its sustainability under destructive impacts.

Analysis of the Information Security Threats in the Digital Production Networks

Abstract: The article analyzes the information security threats arising when constructing digital production networks; presents the problems, which the cybersecurity control system should solve; and proposes an approach for implementing such systems.

Deep Multi-Feature Learning for Water Body Extraction from Landsat Imagery

Abstract: Water body extraction from remote sensing image data has been becoming a really hot topic. Recently, researchers put forward numerous methods for water body extraction, while most of them rely on elaborative feature selection and enough number of training samples. Convolution Neural Network (CNN), one of the implementation models of deep learning, has strong capability for two-dimension images’ classification. A new water body extraction model based on CNNs is established for deep multi-feature learning. Before experiment, image enhancement will be done by Dark Channel Prior. Then we concatenate three kinds of features: spectral information, spatial information that is extracted by Extended Multi-attribute Profile (EMAP) and various water indexes firstly. Next, feature matrixes are acted as the input of CNN-based model for training and classifying. The experimental results showed that the proposed model has better classification performance than Support Vector Machine (SVM) and artificial neural network (ANN). On very limited training set, our model could learn unique and representative features for better water body extraction.

A New Approach for Nonlinear Multivariable Fed-Batch Bioprocess Trajectory Tracking Control

Abstract: This paper proposes a new control law based on linear algebra. This technique allows nonlinear path tracking in multivariable and complex systems. This new methodology consists in finding the control action to make the system follow predefined concentration profiles solving a system of linear equations. The controller parameters are selected with a Monte Carlo algorithm so as to minimize a previously defined cost index. The control scheme is applied to a fed-batch penicillin production process. Different tests are shown to prove the controller effectiveness, such as adding parametric uncertainty, perturbations in the control action and in the initial conditions. Moreover, a comparison with other controllers from the literature is made, showing the better performance of the present approach.

Denoising ECG Signals by Using Extended Kalman Filter to Train Multi-Layer Perceptron Neural Network

Abstract: The purpose of this paper is to study a denoising scheme for ECG signals by using extended Kalman filter based on Multilayer Perceptron Neural Network. A comparison with other enhancement conventional filters, such as, Wiener, wavelet, median and least mean square filters has been investigated. This approach is evaluated on several ECG by artificially adding white and colored Gaussian noises, and real non-stationary muscle artifact to visually inspect clean ECG recordings. It is also evaluated on studying the mean square error and Peak signal to noise ratio of the filters outputs. On the basis of these two parameters, a comparative analysis has been presented to explore the efficient denoising capability of the proposed method. The results of this simulation show the effectiveness of this approach.

Hierarchical Software-Defined Security Management for Large-Scale Dynamic Networks

Abstract: An approach is presented to the construction of a hierarchical security management system for large-scale dynamic communication networks (MANET, VANET, FANET, WSN, etc.) with the use of software-defined network technologies and supercomputer elastic computations. The results of experiments to evaluate the effectiveness of the proposed approach are presented.

Model Order Reduction of Discrete-Time Interval Systems by Differentiation Calculus

Abstract: The paper presents an algorithm for order reduction of a discrete-time interval system based on the conventional differential calculus approach. The procedure initiated for the continuous-time non-interval system advances to the discrete-time interval system in a novel manner in this paper. The proposed algorithm is computationally straightforward, simple and leads to an acceptable results as compared to the existing techniques. The examples play a significant role in establishment of the algorithm. Additionally, the limitation derived during the discourse of methodology is also accounted along with a possible future scope.

A Hybrid Genetic and Ant Colony Algorithm for Finding the Shortest Path in Dynamic Traffic Networks

Abstract: Solving the dynamic shortest path problem has become important in the development of intelligent transportation systems due to the increasing use of this technology in supplying accurate traffic information. This paper focuses on the problem of finding the dynamic shortest path from a single source to a destination in a given traffic network. The goal of our studies is to develop an algorithm to optimize the journey time for the traveler when traffic conditions are in a state of dynamic change. In this paper, the models of the dynamic traffic network and the dynamic shortest path were investigated. A novel dynamic shortest path algorithm based on hybridizing genetic and ant colony algorithms was developed, and some improvements in the algorithm were made according to the nature of the dynamic traffic network. The performance of the hybrid algorithm was demonstrated through an experiment on a real traffic network. The experimental results proved that the algorithm proposed in this paper could effectively find the optimum path in a dynamic traffic network. This algorithm may be useful for vehicle navigation in intelligent transportation systems.

Application of Systems Theory Laws for Investigating Information Security Problems

Abstract: This paper proposes to apply laws of systems theory to study the problems of information security and substantiates the importance of taking into account the social effects of cyberspace development.

Task Allocation Methods for Homogeneous Multi-Robot Systems: Feed Pushing Case Study

Abstract: The paper discuses several options to implement multi-robot systems focusing on coordination and action planning of the system. Particular implementation described in details as a case study presents a solution used in multi-robot system for feed pushing in cattle farm. The paper describes implementation of behavior based robot team control and two different path planning methods ensuring that the feed pushing work is done properly. Conclusions outline suggestions for practical implementations regardless of particular application domain.

FCN and LSTM Based Computer Vision System for Recognition of Vehicle Type, License Plate Number, and Registration Country

Abstract: We propose an advanced Automatic number-plate recognition (ANPR) system, which not only recognizes the number and the issuing state, but also the type and location of the vehicle in the input image. The system is based on a combination of existing methods, modifications to neural network architectures and improvements in the training process. The proposed system uses machine-learning approach and consists of three main parts: segmentation of input image by Fully Convolutional Network for localization of license plate and determination of vehicle type; recognition of the characters of the localized plate by a Maxout CNN and LSTM; determination of the state that has issued the license plate by a CNN. The training of these neural network models is accomplished using a manually labeled custom dataset, which is expanded with data augmented techniques. The resulting system is capable of localizing and classifying multiple types of vehicles (including motorcycles and emergency vehicles) as well as their license plates. The achieved precision of the localization is 99.5%. The whole number recognition accuracy is 96.7% and character level recognition accuracy is 98.8%. The determination of issuing state is precise in 92.8% cases.

Design of Sensor Data Fusion Algorithm for Mobile Robot Navigation Using ANFIS and Its Analysis Across the Membership Functions

Abstract: Design and development of autonomous mobile robots attracts more attention in the era of autonomous navigation. There are various algorithms used in practice for solving research problems related to the robot model and its operating environment. This paper presents the design of data fusion algorithm using Adaptive Neuro Fuzzy Interface (ANFIS) for the navigation of mobile robots. Detailed analysis of various membership functions (MFs) provided in this paper helps to select the most appropriate MF for the design of similar navigation systems. The combined use of fuzzy and neural networks in ANFIS makes the measured distance value of the residual covariance consistent with its actual value. The data fusion algorithm within the controller of the mobile robot fuses the input from ultrasonic and infrared sensors for better environment perception. The results indicate that the data fusion algorithm provides minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared with that of the individual sensors.

On Optimal Interpolation by Linear Functions on n -Dimensional Cube

Abstract: Let $$n \in N$$ , and let $${{Q}_{n}}$$ be the unit cube $${{[0,1]}^{n}}$$ . By $$C({{Q}_{n}})$$ we denote the space of continuous functions $$f:{{Q}_{n}} \to R$$ with the norm $${{\left\| f \right\|}_{{C({{Q}_{n}})}}}: = \mathop {max}\limits_{x \in {{Q}_{n}}} \left| {f(x)} \right|,$$ by $${{\Pi }_{1}}\left( {{{R}^{n}}} \right)$$ – the set of polynomials of $$n$$ variables of degree $$ \leqslant 1$$ (or linear functions). Let $${{x}^{{(j)}}},$$ $$1 \leqslant j \leqslant n + 1,$$ be the vertices of an $$n$$ -dimnsional nondegenerate simplex $$S \subset {{Q}_{n}}$$ . The interpolation projector $$P:C({{Q}_{n}}) \to {{\Pi }_{1}}({{R}^{n}})$$ corresponding to the simplex $$S$$ is defined by the equalities $$Pf\left( {{{x}^{{(j)}}}} \right) = f\left( {{{x}^{{(j)}}}} \right).$$ The norm of $$P$$ as an operator from $$C({{Q}_{n}})$$ to $$C({{Q}_{n}})$$ can be calculated by the formula $$\left\| P \right\| = \mathop {max}\limits_{x \in {\text{ver}}({{Q}_{n}})} \sum olimits_{j = 1}^{n + 1} {\left| {{{\lambda }_{j}}(x)} \right|} .$$ Here $${{\lambda }_{j}}$$ are the basic Lagrange polynomials with respect to $$S,$$ $${\text{ver}}({{Q}_{n}})$$ is the set of vertices of $${{Q}_{n}}$$ . Let us denote by $${{\theta }_{n}}$$ the minimal possible value of $$\left\| P \right\|.$$ Earlier the first author proved various relations and estimates for values $$\left\| P \right\|$$ and $${{\theta }_{n}}$$ , in particular, having geometric character. The equivalence $${{\theta }_{n}} \asymp \sqrt n $$ takes place. For example, the appropriate according to dimension $$n$$ inequalities can be written in the form $$\tfrac{1}{4}\sqrt n $$ $$ < {{\theta }_{n}}$$ $$ < 3\sqrt n .$$ If the nodes of a projector $$P{\text{*}}$$ coincide with vertices of an arbitrary simplex with maximum possible volume, then we have $$\left\| {P{\text{*}}} \right\| \asymp {{\theta }_{n}}.$$ When an Hadamard matrix of order $$n + 1$$ exists, holds $${{\theta }_{n}} \leqslant \sqrt {n + 1} .$$ In the present paper, we give more precise upper bounds of $${{\theta }_{n}}$$ for $$21 \leqslant n \leqslant 26$$ . These estimates were obtained with application of maximum volume simplices in the cube. For constructing such simplices, we utilize maximum determinants containing the elements $$ \pm 1.$$ Also we systematize and comment the best nowaday upper and low estimates of $${{\theta }_{n}}$$ for concrete $$n.$$

Assurance of Cyber Resistance of the Distributed Data Storage Systems Using the Blockchain Technology

Abstract: The article studies the architectures of the modern systems of decentralized data storage and processing, applicability of the blockchain technology in these systems, existing security threats in comparison with the decentralized systems, and methods of security assurance allowing avoidance of these threats.